PhD Chapter 3

Results 2/3

This series of files compile all analyses done during Chapter 3:

All analyses have been done with R 4.1.0.

Click on the table of contents in the left margin to assess a specific analysis.
Click on a figure to zoom it

⏪ | 🏠 | ⏩

1. Spatial variation of exposure indices

Here, we compute semivariograms for each exposure index (on the whole raster, not only extracted values at the stations).

Aquaculture

Dredging

Runoff

Sewers

Structures

Shipping

Fisheries

2. Relationships with abiotic parameters and biotic indices

Biotic indices have been calculated during Chapter 2.

2.1. Covariation

Several types of models were considered to explore relationships: linear, quadratic, exponential and logarithmic. The model with the highest \(R^{2}\) is presented on each plot.

⚠️ Only linear models are implemented now, as there are some bugs with the automatized calculation of the others.

Aquaculture

Dredging

Runoff

Sewers

Structures

Shipping

Fisheries

Cumulative exposure

2.2. Correlation

Correlations have been calculated with Spearman’s rank coefficient.

Correlation coefficients between exposure and variables/indices
  om gravel sand silt clay arsenic cadmium chromium copper iron manganese mercury lead zinc N B W S H margalef lambda J delta FR FE FD AMBI M_AMBI BENTIX BOPA
aquaculture -0.088 0.065 0.023 -0.025 0.051 -0.282 -0.24 -0.355 -0.476 -0.483 -0.479 -0.286 -0.267 -0.413 -0.02 -0.055 0.158 0.152 0.209 0.223 0.191 0.157 0.262 0.204 0.18 -0.14 0.036 0.068 -0.01 -0.192
dredging 0.238 -0.02 -0.012 0.003 0.044 0.062 0.004 0.257 0.373 0.566 0.369 -0.025 0.126 0.28 -0.167 0.107 0.059 -0.164 -0.05 -0.132 -0.019 0.052 -0.027 -0.087 0.074 0.081 0.06 -0.115 -0.015 0.155
runoff -0.016 -0.075 0.297 -0.194 0.018 -0.149 -0.093 0.048 0.299 0.278 0.154 -0.126 -0.027 0.147 -0.059 -0.068 -0.028 -0.162 -0.065 -0.181 -0.012 0.03 -0.085 -0.158 -0.034 0.112 0.01 0.011 -0.004 0.031
sewers 0.424 -0.14 -0.387 0.344 0.164 0.639 0.588 0.691 0.751 0.669 0.755 0.642 0.706 0.743 -0.091 0.053 -0.171 -0.248 -0.23 -0.287 -0.194 -0.108 -0.273 -0.238 -0.187 0.176 -0.036 -0.177 0.205 0.253
structures 0.172 -0.071 0.045 -0.006 0.076 0.052 0.044 0.277 0.465 0.514 0.404 0.057 0.165 0.326 -0.141 0.003 -0.031 -0.219 -0.109 -0.225 -0.054 0.012 -0.122 -0.174 -0.035 0.047 0.039 -0.089 0.042 0.154
shipping 0.371 -0.223 -0.228 0.238 -0.08 0.415 0.27 0.464 0.573 0.57 0.55 0.406 0.415 0.542 -0.071 0.054 -0.003 -0.059 -0.035 -0.059 -0.037 -0.036 -0.082 -0.178 -0.095 0.315 -0.045 -0.085 0.234 0.167
fisheries -0.492 0.202 0.376 -0.378 -0.138 -0.567 -0.541 -0.552 -0.606 -0.576 -0.585 -0.54 -0.563 -0.613 0.173 -0.066 0.097 0.309 0.224 0.28 0.167 -0.015 0.191 0.318 0.082 -0.267 0.143 0.203 -0.226 -0.186
cumulative_exposure 0.277 -0.108 -0.058 0.086 0.087 0.19 0.144 0.357 0.556 0.58 0.471 0.196 0.298 0.445 -0.092 0.03 -0.053 -0.164 -0.107 -0.185 -0.088 -0.048 -0.17 -0.145 -0.075 0.215 0.033 -0.108 0.119 0.182
p-values of correlation test between exposure indices and variables/indices
  om gravel sand silt clay arsenic cadmium chromium copper iron manganese mercury lead zinc N B W S H margalef lambda J delta FR FE FD AMBI M_AMBI BENTIX BOPA
aquaculture 0.3664 0.5051 0.8152 0.7992 0.6021 0.003082 0.01234 0.0001626 1.949e-07 1.228e-07 1.579e-07 0.002721 0.005256 8.895e-06 0.8385 0.5693 0.1014 0.1157 0.02961 0.02045 0.04805 0.1049 0.006131 0.03454 0.06279 0.1478 0.7139 0.4855 0.9216 0.04691
dredging 0.01324 0.8387 0.9003 0.9732 0.6495 0.5259 0.9659 0.00727 6.998e-05 1.771e-10 8.557e-05 0.799 0.1938 0.003346 0.08431 0.269 0.5434 0.09076 0.6071 0.1748 0.8468 0.594 0.7845 0.3704 0.446 0.4042 0.535 0.2368 0.8796 0.1081
runoff 0.8701 0.4395 0.001802 0.04443 0.8515 0.1241 0.3393 0.623 0.001656 0.003518 0.1125 0.1942 0.7832 0.1285 0.5414 0.4829 0.7721 0.09401 0.5056 0.06025 0.9 0.7592 0.38 0.1029 0.7294 0.2487 0.9181 0.9107 0.9663 0.7508
sewers 4.879e-06 0.1485 3.54e-05 0.0002644 0.09067 9.873e-14 2.137e-11 1.233e-16 7.406e-21 2.565e-15 3.667e-21 6.987e-14 1.478e-17 3.514e-20 0.3471 0.5842 0.07716 0.00972 0.01674 0.002579 0.04455 0.2654 0.004234 0.01294 0.05204 0.06905 0.7087 0.06619 0.03313 0.008293
structures 0.07492 0.4645 0.6446 0.9475 0.4369 0.5895 0.6483 0.003746 3.933e-07 1.315e-08 1.425e-05 0.5586 0.08784 0.0005685 0.1453 0.9766 0.7536 0.02293 0.2634 0.01934 0.576 0.8993 0.2088 0.07181 0.7189 0.6262 0.6861 0.3619 0.6684 0.1117
shipping 7.89e-05 0.02058 0.01742 0.01331 0.4089 7.945e-06 0.004671 4.107e-07 9.46e-11 1.253e-10 7.297e-10 1.302e-05 7.806e-06 1.344e-09 0.4653 0.5813 0.9789 0.5475 0.7225 0.5432 0.7071 0.7092 0.3975 0.06563 0.3268 0.0008844 0.6448 0.3801 0.01464 0.08464
fisheries 6.275e-08 0.03585 6.17e-05 5.585e-05 0.1551 1.607e-10 1.476e-09 6.146e-10 3.727e-12 6.679e-11 2.989e-11 1.623e-09 2.366e-10 1.852e-12 0.07296 0.496 0.3185 0.001128 0.01998 0.003357 0.0847 0.8735 0.0474 0.0007943 0.3984 0.005238 0.1407 0.03473 0.01884 0.05423
cumulative_exposure 0.003733 0.2652 0.5492 0.3756 0.3707 0.04849 0.1364 0.0001492 4.022e-10 4.899e-11 2.628e-07 0.04167 0.001703 1.356e-06 0.3421 0.7614 0.588 0.08962 0.2702 0.05549 0.3636 0.6214 0.07827 0.1342 0.4407 0.02571 0.732 0.2679 0.2197 0.05903

3. Relationships with benthic communities

3.1. Species

The most abundant taxa in our study area were:

  • Density: B.neotena (1969), E. integra (1158), P.grandimana (1092), Nematoda (1044) and M. calcarea (575)
  • Biomass: E. parma (biomass of 531.5), Strongylocentrotus sp. (65.3), N. incisa (58.5), M. calcarea (45.4) and S. groenlandicus (34.3)

The following graphs present the distribution of sampled phyla along index of cumulative exposure, according to density (left side) or biomass (right side).

We analyzed if some phyla or species were characteristic of exposure classes, and we calculated the IndVal for each class.

Mean density by class
Phylum high good moderate poor bad
Annelida 27.9 34.1 24.3 NA NA
Arthropoda 29.9 62.7 24.4 NA NA
Cnidaria 0.025 0 0 NA NA
Echinodermata 5.55 1.92 0.389 NA NA
Mollusca 14.8 13.5 11.5 NA NA
Nematoda 20 4.9 0 NA NA
Nemertea 0.3 0.08 0 NA NA
Sipuncula 0.175 0.4 0.111 NA NA
Total individuals by class
Phylum high good moderate poor bad
Annelida 1116 1703 437 0 0
Arthropoda 1195 3136 439 0 0
Cnidaria 1 0 0 0 0
Echinodermata 222 96 7 0 0
Mollusca 590 677 207 0 0
Nematoda 799 245 0 0 0
Nemertea 12 4 0 0 0
Sipuncula 7 20 2 0 0
Mean biomass by class
Phylum high good moderate poor bad
Annelida 0.49 0.855 4.03 NA NA
Arthropoda 0.158 0.114 0.0778 NA NA
Cnidaria 0.0841 0 0 NA NA
Echinodermata 11.4 1.25 4.5 NA NA
Mollusca 1.11 1.56 1.75 NA NA
Nematoda 0.000867 0.000286 0 NA NA
Nemertea 5.5e-05 0.0342 0 NA NA
Sipuncula 0.0114 0.0111 0.00468 NA NA
Total biomasses by class
Phylum high good moderate poor bad
Annelida 19.6 42.7 72.6 0 0
Arthropoda 6.33 5.71 1.4 0 0
Cnidaria 3.36 0 0 0 0
Echinodermata 454 62.4 81.1 0 0
Mollusca 44.4 77.9 31.5 0 0
Nematoda 0.0347 0.0143 0 0 0
Nemertea 0.0022 1.71 0 0 0
Sipuncula 0.457 0.554 0.0842 0 0 

##                   cluster indicator_value probability
## harpacticoida           1          0.3173       0.034
## nematoda                2          0.4158       0.002
## ameritella_agilis       2          0.1612       0.015
## nephtyidae_spp          2          0.1571       0.023
## byblis_gaimardii        2          0.1000       0.036
## 
## Sum of probabilities                 =  67.21 
## 
## Sum of Indicator Values              =  9.67 
## 
## Sum of Significant Indicator Values  =  1.15 
## 
## Number of Significant Indicators     =  5 
## 
## Significant Indicator Distribution
## 
## 1 2 
## 1 4

3.2. Community characteristics

The following graphs present the distribution of community characteristics along index of cumulative exposure.

We also analyzed if the average of each characteristic could be caracteristic of exposure classes.

4. Regressions

For the following analyses, independant variables are abiotic parameters and exposure indices, dependant variables are community characteristics. Variables have been standardized by mean and standard-deviation.

4.1. Data manipulation

All stations and predictors were selected for the regressions, as we are interested in each of them (following graphs are for information only).

Correlation coefficients between exposure indices
  aquaculture dredging runoff sewers structures shipping fisheries
aquaculture 1 -0.291 -0.536 -0.572 -0.498 -0.485 0.456
dredging -0.291 1 0.595 0.409 0.776 0.463 -0.28
runoff -0.536 0.595 1 0.37 0.866 0.288 -0.302
sewers -0.572 0.409 0.37 1 0.601 0.735 -0.686
structures -0.498 0.776 0.866 0.601 1 0.469 -0.377
shipping -0.485 0.463 0.288 0.735 0.469 1 -0.53
fisheries 0.456 -0.28 -0.302 -0.686 -0.377 -0.53 1

4.2. Univariate regressions

We used linear models for the regressions on community characteristics. Variables have been standardized by mean and standard-deviation (coefficients need to be back-transformed to be used in predictive models). Variable selection was not needed here, as we are interested in all exposure indices.

Results of regressions (coefficients with a significant p-value for marginal tests) are shown below. Using both abiotic parameters and exposure indices as predictors do not increase significantly predictive power compared to the other models. Details of the regressions for exposure indices, with diagnostics and cross-validation, are summarized below.

Predictor N B S H J AMBI M-AMBI BENTIX BOPA
Depth + +
Aquaculture
Dredging +
Runoff - +
Sewers - -
Structures +
Shipping + +
Fisheries +
Adjusted \(R^{2}\) 0.02 0.04 0.16 0.29 0.14 0 0.04 0.01 0.13
Density
## Adjusted R2 is: 0.02
Fitting linear model: N ~ depth + aquaculture + dredging + runoff + sewers + structures + shipping + fisheries
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.925e-16 0.09539 2.018e-15 1
depth -0.185 0.1122 -1.649 0.1024
aquaculture 0.05215 0.1347 0.3871 0.6995
dredging -0.1216 0.129 -0.9424 0.3483
runoff 0.1879 0.2201 0.854 0.3952
sewers 0.1503 0.1855 0.8101 0.4198
structures -0.1768 0.2539 -0.6963 0.4879
shipping -0.09248 0.133 -0.6955 0.4884
fisheries 0.122 0.1151 1.06 0.2918 
## RMSE from cross-validation: 1.06337
Variance Inflation Factors
  depth aquaculture dredging runoff sewers structures shipping fisheries
VIF 1.17 1.41 1.35 2.3 1.94 2.65 1.39 1.2

Biomass
## Adjusted R2 is: 0.04
Fitting linear model: B ~ depth + aquaculture + dredging + runoff + sewers + structures + shipping + fisheries
  Estimate Std. Error t value Pr(>|t|)
(Intercept) -6.466e-17 0.09425 -6.861e-16 1
depth -0.2062 0.1109 -1.86 0.06585
aquaculture -0.2534 0.1331 -1.904 0.05983
dredging -0.009733 0.1275 -0.07637 0.9393
runoff -0.4785 0.2174 -2.201 0.03008 *
sewers -0.5772 0.1833 -3.149 0.002167 * *
structures 0.5394 0.2509 2.15 0.03399 *
shipping 0.09399 0.1314 0.7154 0.4761
fisheries -0.09747 0.1138 -0.8568 0.3936 
## RMSE from cross-validation: 1.018014
Variance Inflation Factors
  depth aquaculture dredging runoff sewers structures shipping fisheries
VIF 1.17 1.41 1.35 2.3 1.94 2.65 1.39 1.2

Richness
## Adjusted R2 is: 0.16
Fitting linear model: S ~ aquaculture + dredging + runoff + sewers + structures + shipping + fisheries
  Estimate Std. Error t value Pr(>|t|)
(Intercept) -2.208e-16 0.0882 -2.504e-15 1
aquaculture 0.1387 0.1246 1.114 0.2681
dredging -0.1659 0.1192 -1.392 0.167
runoff 0.1802 0.2009 0.8969 0.3719
sewers -0.3168 0.1586 -1.998 0.04846 *
structures -0.05714 0.2315 -0.2468 0.8056
shipping 0.3368 0.1164 2.894 0.004668 * *
fisheries 0.2269 0.106 2.141 0.03474 * 
## RMSE from cross-validation: 0.945198
Variance Inflation Factors
  aquaculture dredging runoff sewers structures shipping fisheries
VIF 1.41 1.34 2.27 1.79 2.61 1.31 1.2

Diversity
## Adjusted R2 is: 0.29
Fitting linear model: H ~ depth + aquaculture + dredging + runoff + sewers + structures + shipping + fisheries
  Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.513e-16 0.08128 -1.861e-15 1
depth 0.535 0.09561 5.596 1.956e-07 * * *
aquaculture 0.1666 0.1148 1.451 0.1499
dredging 0.005753 0.1099 0.05234 0.9584
runoff 0.4163 0.1875 2.22 0.02868 *
sewers 0.002447 0.1581 0.01548 0.9877
structures -0.3441 0.2163 -1.59 0.115
shipping 0.1112 0.1133 0.9812 0.3289
fisheries 0.02589 0.0981 0.2639 0.7924 
## RMSE from cross-validation: 0.889925
Variance Inflation Factors
  depth aquaculture dredging runoff sewers structures shipping fisheries
VIF 1.17 1.41 1.35 2.3 1.94 2.65 1.39 1.2

Evenness
## Adjusted R2 is: 0.14
Fitting linear model: J ~ depth + aquaculture + dredging + runoff + sewers + structures + shipping + fisheries
  Estimate Std. Error t value Pr(>|t|)
(Intercept) -5.164e-16 0.08921 -5.789e-15 1
depth 0.4573 0.1049 4.358 3.213e-05 * * *
aquaculture 0.06496 0.126 0.5156 0.6073
dredging 0.1359 0.1206 1.126 0.2627
runoff 0.3196 0.2058 1.553 0.1236
sewers 0.06674 0.1735 0.3846 0.7013
structures -0.3271 0.2375 -1.377 0.1715
shipping -0.04701 0.1244 -0.3779 0.7063
fisheries -0.1252 0.1077 -1.163 0.2478 
## RMSE from cross-validation: 0.986413
Variance Inflation Factors
  depth aquaculture dredging runoff sewers structures shipping fisheries
VIF 1.17 1.41 1.35 2.3 1.94 2.65 1.39 1.2

AMBI
## Adjusted R2 is: -0.03
Fitting linear model: AMBI ~ aquaculture + dredging + runoff + sewers + structures + shipping + fisheries
  Estimate Std. Error t value Pr(>|t|)
(Intercept) -2.507e-16 0.09773 -2.565e-15 1
aquaculture -0.06949 0.138 -0.5035 0.6157
dredging 0.06339 0.132 0.4801 0.6322
runoff -0.1955 0.2226 -0.8784 0.3818
sewers -0.06005 0.1757 -0.3417 0.7333
structures 0.2331 0.2565 0.9086 0.3658
shipping -0.1358 0.1289 -1.053 0.2947
fisheries 0.03426 0.1174 0.2917 0.7711 
## RMSE from cross-validation: 1.047711
Variance Inflation Factors
  aquaculture dredging runoff sewers structures shipping fisheries
VIF 1.41 1.34 2.27 1.79 2.61 1.31 1.2

M-AMBI
## Adjusted R2 is: 0.04
Fitting linear model: M_AMBI ~ aquaculture + dredging + runoff + sewers + structures + shipping + fisheries
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.465e-16 0.09426 2.616e-15 1
aquaculture 0.1612 0.1331 1.211 0.2287
dredging -0.2092 0.1274 -1.643 0.1036
runoff 0.4025 0.2147 1.875 0.06374
sewers -0.04131 0.1695 -0.2437 0.8079
structures -0.231 0.2474 -0.9338 0.3526
shipping 0.1189 0.1244 0.9562 0.3413
fisheries 0.09677 0.1133 0.8543 0.395 
## RMSE from cross-validation: 1.009459
Variance Inflation Factors
  aquaculture dredging runoff sewers structures shipping fisheries
VIF 1.41 1.34 2.27 1.79 2.61 1.31 1.2

BENTIX
## Adjusted R2 is: 0.01
Fitting linear model: BENTIX ~ aquaculture + dredging + runoff + sewers + structures + shipping + fisheries
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.237e-16 0.09562 2.339e-15 1
aquaculture 0.1808 0.135 1.339 0.1835
dredging -0.02571 0.1292 -0.199 0.8427
runoff 0.33 0.2178 1.515 0.1328
sewers 0.1123 0.1719 0.6532 0.5151
structures -0.2833 0.251 -1.129 0.2616
shipping 0.2649 0.1262 2.1 0.03828 *
fisheries 0.02871 0.1149 0.2499 0.8032 
## RMSE from cross-validation: 1.034627
Variance Inflation Factors
  aquaculture dredging runoff sewers structures shipping fisheries
VIF 1.41 1.34 2.27 1.79 2.61 1.31 1.2

BOPA
## Adjusted R2 is: 0.13
Fitting linear model: BOPA ~ aquaculture + dredging + runoff + sewers + structures + shipping + fisheries
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 6.155e-17 0.08975 6.858e-16 1
aquaculture 0.02663 0.1268 0.2101 0.834
dredging 0.4859 0.1213 4.007 0.0001184 * * *
runoff 0.02979 0.2044 0.1457 0.8844
sewers 0.1388 0.1614 0.86 0.3918
structures -0.2214 0.2356 -0.9398 0.3496
shipping 0.02867 0.1184 0.2421 0.8092
fisheries 0.02777 0.1079 0.2574 0.7974 
## RMSE from cross-validation: 1.056417
Variance Inflation Factors
  aquaculture dredging runoff sewers structures shipping fisheries
VIF 1.41 1.34 2.27 1.79 2.61 1.31 1.2

4.3. Multivariate regressions

Single
Aquaculture
## Adjusted R2 is: 0.01
## Permutation test for dbrda under reduced model
## Permutation: free
## Number of permutations: 999
## 
## Model: dbrda(formula = sp_L ~ aquaculture, data = var_full_S, distance = "bray")
##           Df SumOfSqs      F Pr(>F)  
## Model      1   0.5405 1.8407  0.034 *
## Residual 106  31.1271                
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Dredging
## Adjusted R2 is: 0.01
## Permutation test for dbrda under reduced model
## Permutation: free
## Number of permutations: 999
## 
## Model: dbrda(formula = sp_L ~ dredging, data = var_full_S, distance = "bray")
##           Df SumOfSqs      F Pr(>F)  
## Model      1   0.6326 2.1605  0.012 *
## Residual 106  31.0351                
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Runoff
## Adjusted R2 is: 0.01
## Permutation test for dbrda under reduced model
## Permutation: free
## Number of permutations: 999
## 
## Model: dbrda(formula = sp_L ~ runoff, data = var_full_S, distance = "bray")
##           Df SumOfSqs      F Pr(>F)  
## Model      1   0.5265 1.7922  0.026 *
## Residual 106  31.1411                
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Sewers
## Adjusted R2 is: 0.04
## Permutation test for dbrda under reduced model
## Permutation: free
## Number of permutations: 999
## 
## Model: dbrda(formula = sp_L ~ sewers, data = var_full_S, distance = "bray")
##           Df SumOfSqs      F Pr(>F)    
## Model      1   1.4864 5.2205  0.001 ***
## Residual 106  30.1812                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Structures
## Adjusted R2 is: 0.01
## Permutation test for dbrda under reduced model
## Permutation: free
## Number of permutations: 999
## 
## Model: dbrda(formula = sp_L ~ structures, data = var_full_S, distance = "bray")
##           Df SumOfSqs      F Pr(>F)  
## Model      1   0.5634 1.9201  0.014 *
## Residual 106  31.1042                
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Shipping
## Adjusted R2 is: 0.05
## Permutation test for dbrda under reduced model
## Permutation: free
## Number of permutations: 999
## 
## Model: dbrda(formula = sp_L ~ shipping, data = var_full_S, distance = "bray")
##           Df SumOfSqs      F Pr(>F)    
## Model      1   1.7855 6.3335  0.001 ***
## Residual 106  29.8822                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Fisheries
## Adjusted R2 is: 0.02
## Permutation test for dbrda under reduced model
## Permutation: free
## Number of permutations: 999
## 
## Model: dbrda(formula = sp_L ~ fisheries, data = var_full_S, distance = "bray")
##           Df SumOfSqs      F Pr(>F)   
## Model      1    0.809 2.7789  0.003 **
## Residual 106   30.859                 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Cumulative exposure
## Adjusted R2 is: 0.02
## Permutation test for dbrda under reduced model
## Permutation: free
## Number of permutations: 999
## 
## Model: dbrda(formula = sp_L ~ cumulative_exposure, data = var_full_S, distance = "bray")
##           Df SumOfSqs      F Pr(>F)    
## Model      1   0.9292 3.2042  0.001 ***
## Residual 106  30.7385                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Multiple

Stations are colored according to the cumulative exposure index (cf. Marine Strategy Framework Directive):

  • indigo = lowest exposure (\(E_{ij}\) < 1.4) ~ high status
  • green = low exposure (1.4 ≤ \(E_{ij}\) < 2.8) ~ good status
  • yellow = moderate exposure (2.8 ≤ \(E_{ij}\) < 4.2) ~ moderate status
  • orange = high exposure (4.2 ≤ \(E_{ij}\) < 5.6) ~ poor status
  • crimson = highest exposure (\(E_{ij}\) ≥ 5.6) ~ bad status
Using vegan

The model has a \(R^{2}\) of 0.23 for exposure indices and 0.34 for all variables.

Using PRIMER-e

The model evaluated by the DistLM procedure has a \(R^{2}\) of 0.22 for exposure indices and 0.34 for all variables.

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